Federated learning (FL), as an emerging collaborative learning paradigm, has
garnered significant attention due to its capacity to preserve privacy within
distributed learning systems. In these systems, clients collaboratively train a
unified neural network model using their local datasets and share model
parameters rather than raw data, enhancing privacy. Predominantly, FL systems
are designed for mobile and edge computing environments where training
typically occurs over wireless networks. Consequently, as model sizes increase,
the conventional FL frameworks increasingly consume substantial communication
resources. To address this challenge and improve communication efficiency, this
paper introduces a novel hierarchical FL framework that integrates the benefits
of clustered FL and model compression. We present an adaptive clustering
algorithm that identifies a core client and dynamically organizes clients into
clusters. Furthermore, to enhance transmission efficiency, each core client
implements a local aggregation with compression (LC aggregation) algorithm
after collecting compressed models from other clients within the same cluster.
Simulation results affirm that our proposed algorithms not only maintain
comparable predictive accuracy but also significantly reduce energy consumption
relative to existing FL mechanisms.

本文介绍了一个集成了聚类式联邦学习和模型压缩优势的新型层次式联邦学习框架，包括自适应聚类算法和局部聚合压缩算法。仿真结果证实，我们提出的算法不仅保持了可比较的预测准确性，还显著降低了相对于现有联邦学习机制的能源消耗。